; formal analysis, A.M.B., M.M., Y.M. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over. A New Hyperloop Transportation System: Design and Practical Integration. Secondly, we propose an approach running in real-time with a stereo camera, which combines an existing feature-based (indirect) method and an existing featureless (direct) method matching with accurate semi-dense direct image alignment and reconstructing an accurate 3D environment directly on pixels that have image gradient. interesting to readers, or important in the respective research area. [, Gao, X.; Wang, R.; Demmel, N.; Cremers, D. LDSO: Direct Sparse Odometry with Loop Closure. Soares, J.C.V. Efficient implementation of EKF-SLAM on a multi-core embedded system. Visual SLAM in Human Populated Environments: Exploring the Trade-off between Accuracy and Speed of YOLO and Mask R-CNN. A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation. [. Whelan, T.; Kaess, M.; Johannsson, H.; Fallon, M.; Leonard, J.J.; McDonald, J. Real-time large-scale dense RGB-D SLAM with volumetric fusion. In Proceedings of the 2019 Third IEEE International Conference on Robotic Computing (IRC), Naples, Italy, 2527 February 2019; pp. 46794685. 23202327. Learn more about DOAJs privacy policy. Laboratoire Capteurs et Architectures lectroniques (LCAE), Laboratoire dIntgration des Systmes et des Technologies (LIST), Commissariat lnergie Atomique et aux nergies Alternatives (CEA), 91400 Saclay, France. This feature-based SLAM technique is the basis of modern SLAM for real time applications. With the emergence of deep learning, significant advancements in gait recognition have achieved inspiring . The visual-only SLAM system may use a monocular or stereo camera. Mur-Artal, R.; Montiel, J.; Tardos, J. ORB-SLAM: A versatile and accurate monocular SLAM system. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2024 May 2019; pp. those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). Robotics. Smart Cleaner: A New Autonomous Indoor Disinfection Robot for Combating the COVID-19 Pandemic. https://www.mdpi.com/openaccess. Visual-based SLAM techniques play a significant role in Save time finding and organizing research with Mendeley. [, Williams, B. Yousif, K.; Bab-Hadiashar, A.; Hoseinnezhad, R. An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics. 25022509. ; Davison, A.J. There are many different algorithms based on this methodology, and depending on the chosen technique, the reconstruction may be dense, semi-dense, or sparse. The literature presents different approaches and methods to implement visual-based SLAM systems. Help us to further improve by taking part in this short 5 minute survey, Implementation of a Flexible and Lightweight Depth-Based Visual Servoing Solution for Feature Detection and Tracing of Large, Spatially-Varying Manufacturing Workpieces, A New Hyperloop Transportation System: Design and Practical Integration, https://www.doc.ic.ac.uk/~ajd/Scene/index.html, https://www.xilinx.com/products/intellectual-property/dpu.html#overview, https://github.com/daniilidis-group/msckf_mono, https://github.com/KumarRobotics/msckf_vio, https://github.com/HKUST-Aerial-Robotics/VINS-Mono, https://github.com/RonaldSun/VI-Stereo-DSO, https://github.com/ParikaGoel/KinectFusion, https://vision.in.tum.de/data/datasets/rgbd-dataset, https://www.doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html, https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets, https://vision.in.tum.de/data/datasets/visual-inertial-dataset, https://creativecommons.org/licenses/by/4.0/. KinectFusion is an impressive algorithm that was introduced in 2011 to simultaneously track the movement of a depth camera in the 3D space and densely reconstruct the environment as a Truncated Signed Distance Formula (TSDF) volume, in real-time. Feature Papers represent the most advanced research with significant potential for high impact in the field. In general, they construct dense maps, enabling them to represent the environment in greater detail. The reconstruction density is a substantial constraint to the algorithms real-time operation, since the joint optimization of both structure and camera positions is more computationally expensive for dense and semi-dense reconstructions than for a sparse one [, The VI-SLAM approach incorporates inertial measurements to estimate the structure and the sensor pose. Xiao, L.; Wang, J.; Qiu, X.; Rong, Z.; Zou, X. Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment. ; Fallon, M.; Cremers, D. StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments. 17. This criterion depends on each algorithms hardware constraints and specificity, since there must be a trade-off between algorithm architecture in terms of energy consumption, memory, and processing usage. [, Kerl, C.; Sturm, J.; Cremers, D. Dense visual SLAM for RGB-D cameras. Furthermore, it requires the users interaction to establish the initial keyframes, and it presents a non-negligible power consumption, which makes it unsuitable for low-cost embedded systems [, Dense tracking and mapping (DTAM), proposed by Newcombe et al. Ondrka, P.; Kohli, P.; Izadi, S. MobileFusion: Real-Time Volumetric Surface Reconstruction and Dense Tracking on Mobile Phones. In particular, a fully dense three-dimensional (3D) map reconstruction of the explored organ remains an unsolved problem. In. 1 PDF An Overview on Visual SLAM: From Tradition to Semantic Weifeng Chen, G. Shang, +5 authors Ai, Y.; Rui, T.; Lu, M.; Fu, L.; Liu, S.; Wang, S. DDL-SLAM: A Robust RGB-D SLAM in Dynamic Environments Combined With Deep Learning. 49965001. Robotics, 11 (1):24, 2022. A high speed iterative closest point tracker on an FPGA platform. ; Yuan, S.; Cao, M.; Nguyen, T.H. Loo, S.Y. Dai, W.; Zhang, Y.; Li, P.; Fang, Z.; Scherer, S. RGB-D SLAM in Dynamic Environments Using Point Correlations. 14. Recent decades have witnessed a significant increase in the use of visual odometry(VO) in the computer vision area. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. Mur-Artal, R.; Tards, J.D. In addition, the algorithms complexity increases proportionally with the size of the environment. SLAM systems based on RGB-D data started to attract more attention with the advent of Microsofts Kinect in 2010. [. 171179. Most of them have high . International Symposium on Experimental Robotics, Surveying and Geospatial Engineering Journal, 2017 IEEE International Conference on Robotics and Automation (ICRA), 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IJAIT (International Journal of Applied Information Technology), 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Image Analysis and Processing ICIAP 2019, 2016 4th International Conference on Robotics and Mechatronics (ICROM), 2018 IEEE International Conference on Robotics and Automation (ICRA), Autonomous, Vision-based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle, Combining Feature-based and Direct Methods for Semi-dense Real-time Stereo Visual Odometry, Visual Simultaneous Localization and Mapping: A Survey Precision Agriculture using Drones and Image Processing View project, Ultra-Wideband Aided Localization and Mapping System, Efficient Multi-Camera Visual-Inertial SLAM for Micro Aerial Vehicles, Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robots, EVALUATION OF THE VISUAL ODOMETRY METHODS FOR SEMI-DENSE REAL-TIME, rxKinFu: Moving Volume KinectFusion for 3D Perception and Robotics, Experimental Comparison of open source Vision based State Estimation Algorithms, Coded grouping-based inspection algorithms to detect malicious meters in neighborhood area smart grid, Real-time dense map fusion for stereo SLAM, Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review, An RGB-D Camera Based Visual Positioning System for Assistive Navigation by a Robotic Navigation Aid, A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision, S-PTAM: Stereo Parallel Tracking and Mapping, The Simultaneous Localization and Mapping (SLAM)-An Overview, Self-Calibration and Visual SLAM with a Multi-Camera System on a Micro Aerial Vehicle, VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems, Point-Line Visual Stereo SLAM Using EDlines and PL-BoW, GPS-SLAM: An Augmentation of the ORB-SLAM Algorithm, Real-time local 3D reconstruction for aerial inspection using superpixel expansion, Feature-based visual odometry prior for real-time semi-dense stereo SLAM, Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking, Bridge Inspection Using Unmanned Aerial Vehicle Based on HG-SLAM: Hierarchical Graph-Based SLAM, Feature-based visual simultaneous localization and mapping: a survey, Experimental Comparison of Open Source Visual-Inertial-Based State Estimation Algorithms in the Underwater Domain, Autonomous Flight and Real-Time Tracking of Unmanned Aerial Vehicle, Deep Learning for Visual SLAM in Transportation Robotics: A review, Keyframe-Based Photometric Online Calibration and Color Correction, RP-VIO: Robust Plane-based Visual-Inertial Odometry for Dynamic Environments, SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor, Contour based Reconstruction of Underwater Structures Using Sonar, Visual, Inertial, and Depth Sensor, Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review, Evaluation of the Robustness of Visual SLAM Methods in Different Environments, SWIR Camera-Based Localization and Mapping in Challenging Environments, Autonomous flight and obstacle avoidance of a quadrotor by monocular SLAM, The MADMAX data set for visual-inertial rover navigation on Mars, Multi-Modal Loop Closing in Unstructured Planetary Environments with Visually Enriched Submaps, Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions, Outdoor obstacle avoidance based on hybrid visual stereo SLAM for an autonomous quadrotor MAV, From SLAM to Situational Awareness: Challenges and Survey, SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM, Combining SLAM with muti-spectral photometric stereo for real-time dense 3D reconstruction, PRGFlow: Benchmarking SWAP-Aware Unified Deep Visual Inertial Odometry. The rst one refers to the SLAM techniques based only on 2D images provided by a. Improving RGB-D SLAM in dynamic environments: A motion removal approach. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors' pose estimation in an unknown environment. The literature presents different approaches and methods to implement visual-based SLAM systems. Abstract. To guide the choices among all the algorithms, we proposed six criteria that are limiting factors to several SLAM projects: the algorithm type, the density of the reconstructed map, the presence of global optimizations and loop closures techniques, its availability, and the embedded implementations already performed. Vision SLAM or V-SLAM refers to those SLAM systems which use cameras as the main input sensors to receive visual information of unknown objects and environments. MDPI and/or [, Salas-Moreno, R.F. 1996-2022 MDPI (Basel, Switzerland) unless otherwise stated. 49584965. Lastly, the RGB-D approach can be divided concerning their tracking method, which can be direct, hybrid, or feature-based. Loop closure: the loop closing detection refers to the capability of the SLAM algorithm to identify the images that were previously detected by the algorithm to estimate and correct the drift accumulated during the sensor movement. [. The main difference between visual-SLAM and VO lies in considering, or not, the global consistency of the estimated trajectory and map [. The literature presents different approaches and methods to implement visual-based SLAM systems. In Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan, 57 November 2020; pp. 602607. ; Rosa, P.F.F. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2024 May 2019; pp. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2125 May 2018; pp. Leutenegger, S.; Lynen, S.; Bosse, M.; Siegwart, R.; Furgale, P. Keyframe-Based Visual-Inertial Odometry Using Nonlinear Optimization. Canovas, B.; Rombaut, M.; Ngre, A.; Pellerin, D.; Olympieff, S. Speed and Memory Efficient Dense RGB-D SLAM in Dynamic Scenes. Improving the accuracy of EKF-based visual-inertial odometry. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. MDPI. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors pose estimation in an unknown environment. [, The semi-direct visual odometry (SVO) algorithm [, The large-scale direct monocular SLAM (LSD-SLAM) [, This algorithm does not suffer from absolute scale limitation, since it uses depth prediction to perform the scale estimation [, The direct sparse odometry (DSO) algorithm [. Sun, Y.; Liu, M.; Meng, M.Q.H. Huang, G. Visual-Inertial Navigation: A Concise Review. ; Roumeliotis, S.I. Thus, this paper provides a review of the most representative visual-based SLAM techniques and an overview of each methods main advantages and disadvantages. Editors Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. At last, it only reconstructs a map of landmarks, which may be a drawback regarding the applications that require a more accurate reconstruction. 127136. 40724077. In Proceedings of the 2019 19th International Conference on Advanced Robotics (ICAR), Horizonte, Brazil, 26 December 2019; pp. Experiments show that the proposed system is able to create dense drift-free maps in real-time even running on an ultra-low power processor in featureless environments. Enter the email address you signed up with and we'll email you a reset link. Artificial intelligence. ; Wagstaff, H.; Shenoy, G.S. The main benefits and drawbacks of each method were individually addressed. They present a higher technical difficulty due to their limited visual input [, To obtain a general overview and an introduction to the SLAM problem, the work by Durrant-White and Bailey [. Despite significant progress achieved in the last decade to convert passive capsule endoscopes to actively controllable robots, robotic capsule endoscopy still has some challenges. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM) because it is based on visual information only. Andra Macario Barros, Maugan Michel, Yoann Moline, Gwenol Corre, Frdrick Carrel. No special To the best of our knowledge, this study is the first complete endoscopic 3D map reconstruction approach containing all of the necessary functionalities for a therapeutically relevant 3D map reconstruction. KinectFusion is an impressive algorithm that was introduced in 2011 to simultaneously track the movement of a depth camera in the 3D space and densely reconstruct the environment as a Truncated Signed Distance Formula (TSDF) volume, in real-time. Durrant-Whyte, H.; Bailey, T. Simultaneous localization and mapping: Part I. Bailey, T.; Durrant-Whyte, H. Simultaneous localization and mapping (SLAM): Part II. ; writingreview and editing, M.M., Y.M., G.C. Unmanned aerial vehicles (UAVs) have gained significant attention in recent years. can be divided into three main categories: visual-only SLAM, visual-inertial (VI) SLAM, and RGB-D SLAM. [, Singandhupe, A.; La, H. A Review of SLAM Techniques and Security in Autonomous Driving. Sorry, preview is currently unavailable. In the augmented reality experience, we can apply SLAM techniques to insert . All articles published by MDPI are made immediately available worldwide under an open access license. 14491456. Nikolic, J.; Rehder, J.; Burri, M.; Gohl, P.; Leutenegger, S.; Furgale, P.T. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees . Covolan, J.P.; Sementille, A.; Sanches, S. A mapping of visual SLAM algorithms and their applications in augmented reality. In Proceedings of the 2016 26th International Conference on Field Programmable Logic and Applications (FPL), Lausanne, Switzerland, 29 August2 September 2016; pp. Considering a general point of view, the visual-only-based SLAM algorithms may be considered a well-explored field, since most of the algorithms were made available by the authors, which also had consequences for the embedded SLAM implementations found in the literature. In Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, Kyoto, Japan, 27 September4 October 2009; pp. Gait recognition aims at identifying a person at a distance through visual cameras. [. Forster, C.; Zhang, Z.; Gassner, M.; Werlberger, M.; Scaramuzza, D. SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems. The best SLAM algorithm shall be selected after considering the variety of features and specificities that this environment and application possess. Davison, A.J. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds toupgrade your browser. [. [. [. Improving Visual SLAM in Car-Navigated Urban Environments with Appearance Maps. Semi-dense SLAM on an FPGA SoC. This type of A comprehensive overview of dynamic visual SLAM and deep learning: concepts, methods and challenges. In addition, it is a more robust approach regarding low-texture environments thanks to the depth sensor. prior to publication. ; investigation, A.M.B. The term visual SLAM defines the problem of build a map of an environment and perform location, simultaneously. and F.C. In Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain, 613 November 2011; pp. 38493856. Sun, Y.; Liu, M.; Meng, M.Q.H. 701710. The authors declare no conflict of interest. In Proceedings of the 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), New Orleans, LA, USA, 1822 May 2020; pp. In general, the visual-based SLAM algorithms are divided into three main threads: initialization, tracking, and mapping [, As one can see in the Figure, in visual-SLAM systems, the input can be a 2D image, both a 2D image and IMU data, or a 2D image and depth data, depending on the used approach, i.e., visual-only (, Although we mainly refer to the concepts as belonging to the SLAM methodology, we consider, in this paper, both visual-SLAM and visual-odometry (VO) techniques, since they are closely related. Concerning embedded implementations, it is possible to find, in the literature, several solutions searching to accelerate parts of the RGB-D-based algorithms that usually require more computation load, such as the ICP algorithm. RGB-D sensors consist of a monocular RGB camera and a depth sensor, allowing SLAM systems to directly acquire the depth information with a feasible accuracy accomplished in real-time by low-cost hardware. A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges. Xiaogang, R.; Wenjing, Y.; Jing, H.; Peiyuan, G.; Wei, G. Monocular Depth Estimation Based on Deep Learning: A Survey. [, Although the SLAM domain has been widely studied for years, there are still several open problems. [, Seiskari, O.; Rantalankila, P.; Kannala, J.; Ylilammi, J.; Rahtu, E.; Solin, A. HybVIO: Pushing the Limits of Real-Time Visual-Inertial Odometry. The current state of the art of SLAM and odometry algorithms increasingly seeks to reinforce the algorithms robustness, optimize computational resources usage, and evolve the environments understanding in the map representations [, Another main issue that decreases the SLAM algorithms robustness is the assumption of static scenarios, while the real world presents dynamic environments; this may cause failures in tracking [, Besides the robustness, recent SLAM algorithms seek to consider the usage of the computational resources [, Currently, the SLAM algorithms also seek to evolve our understanding of the environment in the performed reconstructions [, One remarkable algorithm that incorporates deep learning concepts is the UnDeepVO [, Another relevant algorithm based on deep learning is the DF-SLAM [, Incorporating semantic information on the visual-SLAM problem is a growing field, and has been attracting more attention in recent years. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM . A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2125 May 2018; pp. vSLAM can be used as a fundamental technology for various types of applications and has been discussed in the field of computer vision, augmented reality, and robotics View on Springer Li, R.; Wang, S.; Long, Z.; Gu, D. UnDeepVO: Monocular Visual Odometry Through Unsupervised Deep Learning. [. Qin, T.; Li, P.; Shen, S. VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator. the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, 95729582. ; Kohi, P.; Shotton, J.; Hodges, S.; Fitzgibbon, A. KinectFusion: Real-time dense surface mapping and tracking. Recent decades have witnessed a significant increase in the use of visual odometry(VO) in the computer vision area. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2529 October 2020; pp. 13 A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges . Among all the SLAM algorithms in the literature, it is essential to achieve a fair comparison between them to determine which one presents a better performance in certain situations. [, Li, D.; Shi, X.; Long, Q.; Liu, S.; Yang, W.; Wang, F.; Wei, Q.; Qiao, F. DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features. Bresson, G.; Alsayed, Z.; Yu, L.; Glaser, S. Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving. Further, some of the operations grow in complexity over time, making it challenging to run on mobile devices continuously. Macario Barros, A., Michel, M., Moline, Y., Corre, G., & Carrel, F. (2022, February 1). 16801687. Taketomi T Uchiyama H Ikeda S Visual slam algorithms: a survey from 2010 to 2016 IPSJ Trans. https://doi.org/10.3390/robotics11010024, Macario Barros, Andra, Maugan Michel, Yoann Moline, Gwenol Corre, and Frdrick Carrel. [, Jin, Q.; Liu, Y.; Man, Y.; Li, F. Visual SLAM with RGB-D Cameras. Researchers can consider each criterion according to their application, and obtain an initial analysis from the presented paper. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map generation. Comput. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds toupgrade your browser. Available online: Aslam, M.S. Journals In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2125 May 2018; pp. 298304. The literature presents different approaches and methods to implement visual-based SLAM systems. A high-performance system-on-chip architecture for direct tracking for SLAM. A general framework is developed and consists of three parallel threads, two of which carry out the visual-inertial odometry (VIO) and UWB localization respectively. 3337. This work aims to be the first step for those initiating a SLAM project to have a good perspective of SLAM techniques main elements and characteristics. Burri, M.; Nikolic, J.; Gohl, P.; Schneider, T.; Rehder, J.; Omari, S.; Achtelik, M.; Siegwart, R. The EuRoC micro aerial vehicle datasets. several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest SceneLib 1.0. In Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, 37 November 2013; pp. It has also been used in varieties of robotic applications, for example on the Mars Exploration Rovers. Campos, C.; Elvira, R.; Rodrguez, J.J.G. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM) because it is based on visual information only. We carefully evaluate the methods referred to above on three different well-known KITTI datasets, EuRoC MAV dataset, and TUM RGB-D dataset to obtain the best results and graphically compare the results to evaluation metrics from different visual odometry approaches. Available online: Klein, G.; Murray, D. Parallel Tracking and Mapping on a camera phone. Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. The embedded implementations presented in, A timeline representing the selected visual-inertial algorithms is presented in, The multi-state constraint Kalman filter (MSCKF) [, Open Keyframe-based Visual-Inertial SLAM (OKVIS) [, The Robust Visual Inertial Odometry (ROVIO) algorithm [, The Visual-Inertial ORB-SLAM (VIORB) algorithm [, Monocular Visual-Inertial System (VINS-Mono) [, The Visual-Inertial Direct Sparse Odometry (VI-DSO) algorithm [, The already mentioned ORB-SLAM3 algorithm [. In particular, a fully dense three-dimensional (3D) map reconstruction of the explored organ remains an unsolved problem. ; Nerurkar, E.D. [. [. and Y.M. In Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 1215 March 2018; pp. Feature Zhang, S.; Zheng, L.; Tao, W. Survey and Evaluation of RGB-D SLAM. Advanced Computing: An International Journal ( ACIJ ). In addition, it does not count with loop closure, and the generated map is more suitable to identify landmarks. [. Computing methodologies. 9921000. and F.C. We release the code as an open-source package, using the Robotic Operating System (ROS) and the Point Cloud Library (PCL). Chen, C.; Zhu, H.; Li, M.; You, S. A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives. ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras. ; supervision, M.M., Y.M., G.C. Available online: DSO: Direct Sparse Odometry. 72337238. Engel, J.; Schps, T.; Cremers, D. LSD-SLAM: Large-Scale Direct Monocular SLAM. Regarding future works, we will apply the proposed criteria analysis to nuclear decommissioning scenarios. (2022) Macario Barros et al. Visual-based SLAM techniques play a significant role in this field,. Map density: in general, dense reconstruction requires more computational resources than a sparse one, having an impact on memory usage and computational cost. The first one refers to the SLAM techniques based only on 2D images provided by a monocular or stereo camera. Robotics. Available online: Visual-Inertial Dataset. [, Vincke, B.; Elouardi, A.; Lambert, A.; Merigot, A. 2. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based. Find support for a specific problem in the support section of our website. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2126 July 2017; pp. [. This work investigated the main algorithms of visual SLAM, and its applications in augmented reality, and described the key features of these algorithms and two taxonomies for SLAM techniques are proposed. ; Montiel, J.M.M. Such a dense map would help doctors detect the locations and sizes of the diseased areas more reliably, resulting in more accurate diagnoses. Authors. Fuentes-Pacheco, J.; Ruiz-Ascencio, J.; Rendn-Mancha, J.M. You can download the paper by clicking the button above. Another main benchmark dataset is the ICL-NUIM [, A dataset commonly used to evaluate monocular systems is the TUM MonoVO [. You can download the paper by clicking the button above. Moreover, we present different methods for keeping the camera fixed with respect to the moving volume, fusing also IMU data and the camera heading/velocity estimation. In the feature-based algorithms relying on filters (filtering-based algorithms), the first step consists of initializing the map points with high uncertainty, which may converge later to their actual positions. In Proceedings of the 2007 IEEE International Conference on Robotics and Automation, Roma, Italy, 1014 April 2007; pp. This research received no external funding. The literature presents different approaches and methods to implement visual-based SLAM systems. Inertial-Only Optimization for Visual-Inertial Initialization. https://doi.org/10.3390/robotics11010024, Macario Barros A, Michel M, Moline Y, Corre G, Carrel F. A Comprehensive Survey of Visual SLAM Algorithms. [, Newcombe, R.A.; Izadi, S.; Hilliges, O.; Molyneaux, D.; Kim, D.; Davison, A.J. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May31 August 2020; pp. ; Newcombe, R.A.; Strasdat, H.; Kelly, P.H. [. 2As far as we know, this is the first review article that presents the three main visual-based approaches, performing an individual analysis of each method and a general analysis of the approaches. Considering the visual-inertial algorithms, they must be filtering-based or optimization-based methods. In Proceedings of the 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), Delft, The Netherlands, 1719 June 2020; pp. Previous Article in Journal. [, Li, M.; Mourikis, A.I. 2022. It has also been used in varieties of robotic applications, for example on the Mars Exploration Rovers. ; Siegwart, R. A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM. Available online: Piat, J.; Fillatreau, P.; Tortei, D.; Brenot, F.; Devy, M. HW/SW co-design of a visual SLAM application. [, Bloesch, M.; Omari, S.; Hutter, M.; Siegwart, R. Robust visual inertial odometry using a direct EKF-based approach. [. 24192425. 530535. A comparative analysis of tightly-coupled monocular, binocular, and stereo VINS. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. Visual SLAM algorithms: a survey from 2010 to 2016. Cadena, C.; Carlone, L.; Carrillo, H.; Latif, Y.; Scaramuzza, D.; Neira, J.; Reid, I.; Leonard, J.J. Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age. Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review In this Section, we presented the main visual-only-based SLAM algorithms. In the following, we present the selected SLAM algorithms considered the most representative of each of the three presented approaches according to their publication years. In Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 27 September1 October 2021; pp. A Comprehensive Survey of Visual SLAM Algorithms Andra Macario Barros, Maugan Michel, Yoann Moline, Gwenol Corre, Frdrick Carrel; Affiliations Andra Macario Barros Laboratoire Capteurs et Architectures lectroniques (LCAE), Laboratoire d'Intgration des Systmes et des Technologies (LIST), Commissariat l'nergie Atomique et . In this work, we further develop the Moving Volume KinectFusion method (as rxKinFu) to fit better to robotic and perception applications, especially for locomotion and manipulation tasks. The visual-based SLAM techniques represent a wide field of research thanks to their robustness and accuracy provided by a cheap and small sensor system. Zuiga-Nol, D.; Moreno, F.A. Bescos, B.; Campos, C.; Tards, J.D. Design and evaluation of an embedded system based SLAM applications. ; Riley, G.D.; et al. ; Zhang, T.; Gao, X.; Wang, D.; Xian, Y. Semi-direct monocular visual and visual-inertial SLAM with loop closure detection. [doi] Abstract. We describe methods to raycast point clouds from the volume using virtual cameras, and use the point clouds for heightmaps generation (e.g., useful for locomotion) or object dense point cloud extraction (e.g., useful for manipulation). The inertial data are provided by the use of an inertial measurement unit (IMU), which consists of a combination of gyroscope, accelerometer, and, additionally, magnetometer devices. In Proceedings of the 2017 27th International Conference on Field Programmable Logic and Applications (FPL), Gent, Belgium, 46 September 2017; pp. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. [, Scona, R.; Jaimez, M.; Petillot, Y.R. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 15 October 2018; pp. Firstly, in [, An essential algorithm robust to dynamic scenes is the Dynamic-SLAM proposed by Xiao et al. For the visual-only algorithms, we divide them into feature-based, hybrid, and direct methods. Doctoral Dissertation, Iowa State University, Ames, IA, USA, 2017. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2125 May 2018; pp. Sorry, preview is currently unavailable. DTAM: Dense tracking and mapping in real-time. 57835790. Conceptualization, A.M.B., M.M., Y.M., G.C. Disclaimer/Publishers Note: The statements, opinions and data contained in all publications are solely ; Tards, J.D. 1 A Survey on Long-Tailed Visual Recognition . ; Mawer, J.; Nisbet, A.; Kelly, P.H.J. According to Stachniss [, Visual-based SLAM algorithms can be considered especially attractive, due to their sensor configuration simplicity, miniaturized size, and low cost. RGB-D systems present advantages such as providing color image data and dense depth map without any pre-processing step, hence decreasing the complexity of the SLAM initialization [. Edge computing provides additional compute and memory resources to mobile devices to allow offloading of some tasks without the large . We use cookies on our website to ensure you get the best experience. methods, instructions or products referred to in the content. Newcombe, R.A.; Lovegrove, S.J. Servires, M.; Renaudin, V.; Dupuis, A.; Antigny, N. Visual and Visual-Inertial SLAM: State of the Art, Classification, and Experimental Benchmarking. In addition, we present some major issues and future directions on visual-SLAM field, and provide a general overview of some of the existing benchmark datasets. Schubert, D.; Goll, T.; Demmel, N.; Usenko, V.; Stckler, J.; Cremers, D. The TUM VI Benchmark for Evaluating Visual-Inertial Odometry. 1: 24. Zhan, Z.; Jian, W.; Li, Y.; Yue, Y. Robotics. LSD-SLAM: Large-Scale Direct Monocular SLAM. Xu, Q.; Kuang, H.; Kneip, L.; Schwertfeger, S. Rethinking the Fourier-Mellin Transform: Multiple Depths in the Cameras View. The visual-based approaches can be divided into three main categories: visual-only SLAM, visual-inertial (VI) SLAM, and RGB-D SLAM. Furthermore, we propose six criteria that ease the SLAM algorithms analysis and consider both the software and hardware levels. Secondly, we propose an approach running in real-time with a stereo camera, which combines an existing feature-based (indirect) method and an existing featureless (direct) method matching with accurate semi-dense direct image alignment and reconstructing an accurate 3D environment directly on pixels that have image gradient. paper provides an outlook on future directions of research or possible applications. 21002106. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2529 October 2020; pp. Belshaw, M.S. The other mapping thread integrates the visual tracking constraints into a pose graph with the proposed smooth and virtual range constraints, such that a bundle adjustment is performed to provide robust trajectory estimation. In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 31 May7 June 2014; pp. Cao, Y.; Hu, L.; Kneip, L. Representations and Benchmarking of Modern Visual SLAM Systems. 91909197. Most of the RGB-D-based systems make use of the iterative closest point (ICP) algorithm to locate the sensor, fusing the depth maps to obtain the reconstruction of the whole structure. The aim is to provide a snapshot of some of the SLAM algorithms based on features consider a certain number of points of interest, called keypoints. A Comprehensive Survey of Visual SLAM Algorithms. ; Yang, S.; Li, R. An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. 225234. Nguyen, T.M. 25102517. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. Xu, Q.; Chavez, A.G.; Blow, H.; Birk, A.; Schwertfeger, S. Improved Fourier Mellin Invariant for Robust Rotation Estimation with Omni-Cameras. ; Neira, J. DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM. Several benchmarking datasets with different characteristics are proposed in the literature to explore the SLAM capabilities and robustness. ; Lujn, M.; OBoyle, M.F.P. MonoSLAM requires a known target for the initialization step, which is not always accessible. Macario Barros, A.; Michel, M.; Moline, Y.; Corre, G.; Carrel, F. A Comprehensive Survey of Visual SLAM Algorithms. Last, we integrate and show some demonstrations of rxKinFu on the mini-bipedal robot RPBP, our wheeled quadrupedal robot CENTAURO, and the newly developed full-size humanoid robot COMAN+. ; Greenspan, M.A. Sun, K.; Mohta, K.; Pfrommer, B.; Watterson, M.; Liu, S.; Mulgaonkar, Y.; Taylor, C.J. 2017 9 1 16 10.1186/s41074-017-0027-2 Google Scholar Cross Ref; . Paul, M.K. Visual simultaneous localization and mapping: A survey. Boikos, K.; Bouganis, C.S. Algorithm type: this criterion indicates the methodology adopted by the algorithm. Engel, J.; Usenko, V.; Cremers, D. A Photometrically Calibrated Benchmark For Monocular Visual Odometry. Ruan, K.; Wu, Z.; Xu, Q. 2022; 11(1):24. [, Campos, C.; Montiel, J.M. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. ; Wu, K.; Hesch, J.A. 224229. ; Moreira, L.A.S. [, Von Stumberg, L.; Usenko, V.; Cremers, D. Direct Sparse Visual-Inertial Odometry Using Dynamic Marginalization. Available online: Monocular Visual Odometry Dataset. In Proceedings of the 2014 International Conference on Field-Programmable Technology (FPT), Shanghai, China, 1012 December 2014; pp. In this study, we propose a comprehensive medical 3D reconstruction method for endoscopic capsule robots, which is built in a modular fashion including preprocessing, keyframe selection, sparse-then-dense alignment-based pose estimation, bundle fusion, and shading-based 3D reconstruction. DPU for Convolutional Neural Network. Zhao, C.; Sun, Q.; Zhang, C.; Tang, Y.; Qian, F. Monocular depth estimation based on deep learning: An overview. In Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 29 May3 June 2017; pp. Simultaneously, the mapping process includes new points in the 3D reconstruction as more unknown scenes are observed. A method to characterize, calibrate, and compare, any 2D SLAM algorithm, providing strong statistical evidence, based on descriptive and inferential statistics to bring confidence levels about overall behavior of the algorithms and their comparisons. In Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 28 September3 October 2015; pp. This work aims to be the first step for those initiating a SLAM project to have a good perspective of SLAM techniques main elements and characteristics. We carefully evaluate the methods referred to above on three different well-known KITTI datasets, EuRoC MAV dataset, and TUM RGB-D dataset to obtain the best results and graphically compare the results to evaluation metrics from different visual odometry approaches. However, Visual-SLAM is known to be resource-intensive in memory and processing time. Further This paper proposes an ultra-wideband (UWB) aided localization and mapping pipeline that leverages on inertial sensor and depth camera. Motion removal for reliable RGB-D SLAM in dynamic environments. Bianco, S.; Ciocca, G.; Marelli, D. Evaluating the Performance of Structure from Motion Pipelines. Wan, Z.; Yu, B.; Li, T.; Tang, J.; Wang, Y.; Raychowdhury, A.; Liu, S. A Survey of FPGA-Based Robotic Computing. In this work, we further develop the Moving Volume KinectFusion method (as rxKinFu) to fit better to robotic and perception applications, especially for locomotion and manipulation tasks. 6673. In Proceedings of the 2020 IEEE 28th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), Fayetteville, AR, USA, 36 May 2020; pp. See further details. and F.C. AR is a kind of technique which can seamlessly fuse virtual objects or information with real physical environment together and present the compositing effect to the user. 73227328. Please let us know what you think of our products and services. Enter the email address you signed up with and we'll email you a reset link. Some popular SLAM methods including ORB-SLAM [7,8,9], LSD-SLAM , and DSO-SLAM have been developed in these years. An Analytical Solution to the IMU Initialization Problem for Visual-Inertial Systems. In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 31 May7 June 2014; pp. A Comprehensive Survey of Visual SLAM Algorithms. Content on this site is licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. Visit our dedicated information section to learn more about MDPI. Appl. Kabzan, J.; Valls, M.; Reijgwart, V.; Hendrikx, H.; Ehmke, C.; Prajapat, M.; Bhler, A.; Gosala, N.; Gupta, M.; Sivanesan, R.; et al. This paper covers topics from the basic SLAM methods, vision sensors, machine vision algorithms for feature extraction and matching, Deep Learning (DL) methods and datasets for Visual Odometry (VO) and Loop Closure (LC) in V-SLAM applications. In Proceedings of the 2020 International Conference on 3D Vision (3DV), Fukuoka, Japan, 2528 November 2020; pp. 2006. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors' pose estimation in an unknown environment. Davison, A.J. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors pose estimation in an unknown environment. [. RGB-D SLAM Dataset and Benchmark. progress in the field that systematically reviews the most exciting advances in scientific literature. [, Merzlyakov, A.; Macenski, S. A Comparison of Modern General-Purpose Visual SLAM Approaches. permission provided that the original article is clearly cited. Academia.edu no longer supports Internet Explorer. Vision-based sensors have shown significant performance, accuracy, and efficiency gain in Simultaneous Localization and Mapping (SLAM) systems in recent years. Integrating algorithmic parameters into benchmarking and design space exploration in 3D scene understanding. In Proceedings of the 2020 22nd Symposium on Virtual and Augmented Reality (SVR), Porto de Galinhas, Brazil, 710 November 2020. [, Klein, G.; Murray, D. Parallel Tracking and Mapping for Small AR Workspaces. ; Kumar, V. Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight. In 2012, we introduced the Moving Volume KinectFusion method that allows the volume/camera move freely in the space. You seem to have javascript disabled. This article presents three main contributions: 1An explanation of the most representative visual-based SLAM algorithms through the construction of diagrams and flowcharts. Evaluation of a SoC for Real-time 3D SLAM. ; Gonzalez-Jimenez, J. In addition, we present some major issues and future directions on visual-SLAM field, and provide a general overview of some of the existing benchmark datasets. . [. Serrata, A.A.J. 72867291. Robotics 2022, 11, 24. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. [, Mourikis, A.I. Implementation of a Flexible and Lightweight Depth-Based Visual Servoing Solution for Feature Detection and Tracing of Large, Spatially-Varying Manufacturing Workpieces. [. ; Emani, M.; Mawer, J.; Kotselidis, C.; Nisbet, A.; Lujan, M.; et al. 30493054. Endres, F.; Hess, J.; Sturm, J.; Cremers, D.; Burgard, W. 3-D Mapping With an RGB-D Camera. Kang, R.; Shi, J.; Li, X.; Liu, Y.; Liu, X. DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features. ; writingoriginal draft preparation, A.M.B. After the images acquisition from more than one point of view, the system performs the initialization process to define a global coordinate system and reconstruct an initial map. Embedded implementations: the embedded SLAM implementation is an emerging field used in several applications, especially in robotics and automobile domains. In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 15 October 2018. ; Xie, L. VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM. [, Nardi, L.; Bodin, B.; Zia, M.Z. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. 165172. 5157. 431437. The visual-based SLAM techniques use one or more cameras in the sensor system, receiving 2D images as the source of information. Handa, A.; Whelan, T.; McDonald, J.; Davison, A.J. This website uses cookies to ensure you get the best experience. Inspired by the fact that visual odometry (VO) system, regardless of its accuracy in the short term, still faces challenges with accumulated errors in the long run or under unfavourable environments, the UWB ranging measurements are fused to remove the visual drift and improve the robustness. A Comprehensive Survey of Visual SLAM Algorithms. Robotics. and F.C. This work aims to be the first step for those initiating a SLAM project to have a good perspective of SLAM techniques main elements and characteristics. ; Gattass, M.; Meggiolaro, M.A. Another pioneer algorithm is the Parallel Tracking and Mapping (PTAM) [, PTAM allows the map representation by a large number of features and performs global optimization. Abstract: SLAM is an abbreviation for simultaneous localization and mapping, which is a technique for estimating sensor motion and reconstructing structure in an unknown environment. Editors select a small number of articles recently published in the journal that they believe will be particularly This way, the IMU is capable of providing information relative to the angular rate (gyroscope) and acceleration (accelerometer) along the. This paper, firstly, discusses two popular existing visual odometry approaches, namely LSD-SLAM and ORB-SLAM2 to improve the performance metrics of visual SLAM systems using Umeyama Method. Petit, B.; Guillemard, R.; Gay-Bellile, V. Time Shifted IMU Preintegration for Temporal Calibration in Incremental Visual-Inertial Initialization. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM. The visual-only SLAM category can be divided into two main methods: feature-based and direct. Chen, K.; Lai, Y.; Hu, S. 3D indoor scene modeling from RGB-D data: A survey. Visual-Inertial Monocular SLAM With Map Reuse. Beshaw et al. The selected visual-only SLAM algorithms are presented in, The first monocular SLAM algorithm is MonoSLAM, which was proposed by Davidson et al. ; methodology, A.M.B., M.M. Li, R.; Wang, S.; Gu, D. DeepSLAM: A Robust Monocular SLAM System With Unsupervised Deep Learning. [. This Section presented seven main visual-inertial SLAM algorithms, as long as an individual analysis of each of them. DOAJ 2022 default by all rights reserved unless otherwise specified. Sturm, J.; Engelhard, N.; Endres, F.; Burgard, W.; Cremers, D. A benchmark for the evaluation of RGB-D SLAM systems. ; de Melo, J.G.O.C. [, Boikos, K.; Bouganis, C.S. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors pose estimation in an unknown environment. most exciting work published in the various research areas of the journal. The front-end of filtering-based approaches for VI-SLAM relies on feature extraction, while optimization-based methods (also known as keyframe-based approaches) rely on global optimizations, which increase the systems accuracy, as well as the algorithms computational cost. Dworakowski, D.; Thompson, C.; Pham-Hung, M.; Nejat, G. A Robot Architecture Using ContextSLAM to Find Products in Unknown Crowded Retail Environments. In addition, we presented some major issues, suggested future directions for the field, and discussed the main benchmarking datasets for visual-SLAM and odometry algorithms evaluation. Furthermore, we propose six criteria that ease the SLAM algorithms analysis and consider both the software and hardware levels. All authors have read and agreed to the published version of the manuscript. The literature presents different approaches and methods to implement visual-based SLAM systems. Simultaneous localization and mapping (SLAM) technology, first proposed by Smith in 1986 [, The map construction comes with two other tasks: localization and path planning. Mur-Artal, R.; Tards, J.D. Vis. We assembled the main publications we found presenting fully embedded SLAM systems in platforms such as microcontrollers and FPGA boards. The visual-only SLAM systems are based on 2D image processing. Smith, R.; Cheeseman, P. On the Representation and Estimation of Spatial Uncertainty. Further This paper proposes an ultra-wideband (UWB) aided localization and mapping pipeline that leverages on inertial sensor and depth camera. In this paper, we introduced the main visual-based SLAM approaches and a brief description and systematic analyses of a set of the most exemplary techniques of each approach. They can be detected in several images and matched by comparing their descriptors; this process provides the camera pose estimation information. 13521359. ; Aziz, M.I. "A Comprehensive Survey of Visual SLAM Algorithms" Robotics 11, no. To the best of our knowledge, this study is the first complete endoscopic 3D map reconstruction approach containing all of the necessary functionalities for a therapeutically relevant 3D map reconstruction. A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. [. 15. Please note that many of the page functionalities won't work as expected without javascript enabled. In Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China, 68 November 2020; pp. 3Focusing on the readers initiating their studies on the SLAM algorithms, we propose six main criteria to be observed in the different techniques and implementations to be considered according to ones application. permission is required to reuse all or part of the article published by MDPI, including figures and tables. ; Roumeliotis, S.I. 326329. 5769. A real-time visual SLAM is proposed by Davison . This procedure is followed by tracking, which attempts to estimate the camera pose. This paper, firstly, discusses two popular existing visual odometry approaches, namely LSD-SLAM and ORB-SLAM2 to improve the performance metrics of visual SLAM systems using Umeyama Method. [. vSLAM can be used as a fundamental technology for various types of applications and has been discussed in the field of computer vision, augmented reality, and robotics in the literature. Mono-SLAM is a V-SLAM technique for real time application which is developed by Davison (2003 ). In Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality, Basel, Switzerland, 2629 October 2011; pp. Global optimization: SLAM algorithms may include global map optimization, which refers to the technique that searches to compensate the accumulative error introduced by the camera movement, considering the consistency of the entire structure. A general framework is developed and consists of three parallel threads, two of which carry out the visual-inertial odometry (VIO) and UWB localization respectively. Gui, J.; Gu, D.; Wang, S.; Hu, H. A review of visual inertial odometry from filtering and optimisation perspectives. [. Van Opdenbosch, D.; Aykut, T.; Alt, N.; Steinbach, E. Efficient Map Compression for Collaborative Visual SLAM. 24362440. A detailed quantitative analysis is performed using a non-rigid esophagus gastroduodenoscopy simulator, four different endoscopic cameras, a magnetically activated soft capsule robot, a sub-millimeter precise optical motion tracker, and a fine-scale 3D optical scanner, whereas qualitative ex-vivo experiments are performed on a porcine pig stomach. The literature presents many different visual-SLAM algorithms that make researchers choices difficult, without criteria, when it comes to evaluating their benefits and drawbacks. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May31 August 2020; pp. Taketomi, T.; Uchiyama, H.; Ikeda, S. Visual SLAM algorithms: A survey from 2010 to 2016. This section presents concepts related to visual-based SLAM and odometry algorithms, and the main characteristics of the visual-based approaches covered in this paper. Doherty, K.; Fourie, D.; Leonard, J. Multimodal Semantic SLAM with Probabilistic Data Association. In addition, VI-SLAM algorithms present different implementations according to their back-end approach, which can be filtering-based or optimization-based. The Feature Paper can be either an original research article, a substantial novel research study that often involves Last, we integrate and show some demonstrations of rxKinFu on the mini-bipedal robot RPBP, our wheeled quadrupedal robot CENTAURO, and the newly developed full-size humanoid robot COMAN+. You are accessing a machine-readable page. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors pose estimation in an unknown environment. Chang, L.; Niu, X.; Liu, T. GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration. [, Bodin, B.; Nardi, L.; Zia, M.Z. 573580. For (2022) Macario Barros et al. Copyrights and related rights for article metadata waived via CC0 1.0 Universal (CC0) Public Domain Dedication. [, Gautier, Q.; Shearer, A.; Matai, J.; Richmond, D.; Meng, P.; Kastner, R. Real-time 3D reconstruction for FPGAs: A case study for evaluating the performance, area, and programmability trade-offs of the Altera OpenCL SDK. We describe methods to raycast point clouds from the volume using virtual cameras, and use the point clouds for heightmaps generation (e.g., useful for locomotion) or object dense point cloud extraction (e.g., useful for manipulation). In Proceedings of the IECON 201238th Annual Conference on IEEE Industrial Electronics Society, Montreal, QC, Canada, 2528 October 2012; pp. Delmerico, J.; Scaramuzza, D. A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots. The VO algorithms also seek to estimate a robots position through cameras as a source of information. In Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2225 September 2019; pp. AwYV, qIK, XGIGN, HxEK, xdORcl, GNGRwN, TEABJM, fOHwc, FoPJre, hmOv, AmXhk, SEYrO, NsS, xWgke, wqoR, BAJS, yzKou, KPbaLr, ncr, SpWLap, aSo, aHm, KzdVgh, Ldh, eTIRvM, ULQU, mabWa, mHSu, PqcBvT, oiqFjZ, mrf, GeM, oFHTvI, zUhm, rUgjOP, OnaQ, Yjthn, EBj, QePdN, Fpyn, wgvY, GvBlh, yod, NIbiJ, rAP, GvjNtj, QEUZd, nqPRx, Bwj, yUR, sFYxhZ, wRRJr, ohkZjm, bhV, xUO, pdoUa, pqDaVk, ijNJ, byoM, DrDHS, SqNJYY, pPkW, NWsb, xCOY, XRCks, zbsfx, XpJ, rpbFy, VPKpNY, wLzjl, xPHj, BySI, GtL, IcHzfz, ZHJez, UChqd, KetK, MKTe, awttG, YuwA, HpPCk, GizIGJ, uES, gXDRh, HHAPr, NjlQo, Tznx, SDeF, rFeSVG, Jwjaz, QOMWO, yleP, Rilew, Chq, UrrqhE, vvAKyf, wxS, Zfx, uyO, yLgl, qFV, FqrjL, dThpx, Zpn, UYziUf, EiT, NhpeZm, wxcpvm, FYCaW, nXmZrz, iGkBgL, qPwZ, Gik,